A Meta-Analysis of Factors Influencing the Development of Trust in Automation

OBJECTIVE: We used meta-analysis to assess research concerning human trust in automation to understand the foundation upon which future autonomous systems can be built. BACKGROUND: Trust is increasingly important in the growing need for synergistic human-machine teaming. Thus, we expand on our previous meta-analytic foundation in the field of human-robot interaction to include all of automation interaction. METHOD: We used meta-analysis to assess trust in automation. Thirty studies provided 164 pairwise effect sizes, and 16 studies provided 63 correlational effect sizes. RESULTS: The overall effect size of all factors on trust development was ḡ = +0.48, and the correlational effect was [Formula: see text] = +0.34, each of which represented medium effects. Moderator effects were observed for the human-related (ḡ = +0.49; [Formula: see text] = +0.16) and automation-related (ḡ = +0.53; [Formula: see text] = +0.41) factors. Moderator effects specific to environmental factors proved insufficient in number to calculate at this time. CONCLUSION: Findings provide a quantitative representation of factors influencing the development of trust in automation as well as identify additional areas of needed empirical research. APPLICATION: This work has important implications to the enhancement of current and future human-automation interaction, especially in high-risk or extreme performance environments.